mirror of
https://github.com/titanscouting/tra-analysis.git
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analysis pkg v 1.0.0.10
analysis.py v 1.1.13.008 superscript.py v 0.0.5.001
This commit is contained in:
ltcptgeneral
parent
5e71d05626
commit
337fae68ee
@ -1,6 +1,6 @@
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Metadata-Version: 2.1
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Name: analysis
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Version: 1.0.0.9
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Version: 1.0.0.10
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Summary: analysis package developed by Titan Scouting for The Red Alliance
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Home-page: https://github.com/titanscout2022/tr2022-strategy
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Author: The Titan Scouting Team
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@ -1,6 +1,7 @@
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setup.py
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analysis/__init__.py
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analysis/analysis.py
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analysis/glicko2.py
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analysis/regression.py
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analysis/titanlearn.py
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analysis/trueskill.py
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# current benchmark of optimization: 1.33 times faster
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# setup:
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__version__ = "1.1.13.007"
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__version__ = "1.1.13.008"
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# changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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1.1.13.008:
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- moved Glicko2 to a seperate package
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1.1.13.007:
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- fixed bug with trueskill
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1.1.13.006:
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@ -271,7 +273,6 @@ __all__ = [
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'SVM',
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'random_forest_classifier',
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'random_forest_regressor',
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'Glicko2',
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# all statistics functions left out due to integration in other functions
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]
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@ -280,6 +281,7 @@ __all__ = [
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# imports (now in alphabetical order! v 1.0.3.006):
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import csv
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from analysis import glicko2 as Glicko2
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import numba
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from numba import jit
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import numpy as np
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@ -452,7 +454,7 @@ def elo(starting_score, opposing_score, observed, N, K):
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def glicko2(starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations):
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player = Glicko2(rating = starting_score, rd = starting_rd, vol = starting_vol)
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player = Glicko2.Glicko2(rating = starting_score, rd = starting_rd, vol = starting_vol)
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player.update_player([x for x in opposing_score], [x for x in opposing_rd], observations)
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@ -690,103 +692,4 @@ def random_forest_regressor(data, outputs, test_size, n_estimators="warn", crite
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kernel.fit(data_train, outputs_train)
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predictions = kernel.predict(data_test)
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return kernel, RegressionMetrics(predictions, outputs_test)
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class Glicko2:
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_tau = 0.5
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def getRating(self):
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return (self.__rating * 173.7178) + 1500
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def setRating(self, rating):
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self.__rating = (rating - 1500) / 173.7178
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rating = property(getRating, setRating)
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def getRd(self):
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return self.__rd * 173.7178
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def setRd(self, rd):
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self.__rd = rd / 173.7178
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rd = property(getRd, setRd)
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def __init__(self, rating = 1500, rd = 350, vol = 0.06):
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self.setRating(rating)
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self.setRd(rd)
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self.vol = vol
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def _preRatingRD(self):
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self.__rd = math.sqrt(math.pow(self.__rd, 2) + math.pow(self.vol, 2))
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def update_player(self, rating_list, RD_list, outcome_list):
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rating_list = [(x - 1500) / 173.7178 for x in rating_list]
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RD_list = [x / 173.7178 for x in RD_list]
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v = self._v(rating_list, RD_list)
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self.vol = self._newVol(rating_list, RD_list, outcome_list, v)
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self._preRatingRD()
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self.__rd = 1 / math.sqrt((1 / math.pow(self.__rd, 2)) + (1 / v))
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tempSum = 0
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for i in range(len(rating_list)):
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tempSum += self._g(RD_list[i]) * \
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(outcome_list[i] - self._E(rating_list[i], RD_list[i]))
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self.__rating += math.pow(self.__rd, 2) * tempSum
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def _newVol(self, rating_list, RD_list, outcome_list, v):
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i = 0
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delta = self._delta(rating_list, RD_list, outcome_list, v)
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a = math.log(math.pow(self.vol, 2))
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tau = self._tau
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x0 = a
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x1 = 0
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while x0 != x1:
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# New iteration, so x(i) becomes x(i-1)
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x0 = x1
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d = math.pow(self.__rating, 2) + v + math.exp(x0)
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h1 = -(x0 - a) / math.pow(tau, 2) - 0.5 * math.exp(x0) \
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/ d + 0.5 * math.exp(x0) * math.pow(delta / d, 2)
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h2 = -1 / math.pow(tau, 2) - 0.5 * math.exp(x0) * \
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(math.pow(self.__rating, 2) + v) \
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/ math.pow(d, 2) + 0.5 * math.pow(delta, 2) * math.exp(x0) \
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* (math.pow(self.__rating, 2) + v - math.exp(x0)) / math.pow(d, 3)
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x1 = x0 - (h1 / h2)
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return math.exp(x1 / 2)
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def _delta(self, rating_list, RD_list, outcome_list, v):
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tempSum = 0
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for i in range(len(rating_list)):
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tempSum += self._g(RD_list[i]) * (outcome_list[i] - self._E(rating_list[i], RD_list[i]))
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return v * tempSum
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def _v(self, rating_list, RD_list):
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tempSum = 0
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for i in range(len(rating_list)):
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tempE = self._E(rating_list[i], RD_list[i])
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tempSum += math.pow(self._g(RD_list[i]), 2) * tempE * (1 - tempE)
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return 1 / tempSum
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def _E(self, p2rating, p2RD):
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return 1 / (1 + math.exp(-1 * self._g(p2RD) * \
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(self.__rating - p2rating)))
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def _g(self, RD):
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return 1 / math.sqrt(1 + 3 * math.pow(RD, 2) / math.pow(math.pi, 2))
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def did_not_compete(self):
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self._preRatingRD()
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return kernel, RegressionMetrics(predictions, outputs_test)
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99
analysis-master/analysis-amd64/analysis/glicko2.py
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99
analysis-master/analysis-amd64/analysis/glicko2.py
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import math
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class Glicko2:
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_tau = 0.5
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def getRating(self):
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return (self.__rating * 173.7178) + 1500
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def setRating(self, rating):
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self.__rating = (rating - 1500) / 173.7178
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rating = property(getRating, setRating)
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def getRd(self):
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return self.__rd * 173.7178
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def setRd(self, rd):
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self.__rd = rd / 173.7178
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rd = property(getRd, setRd)
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def __init__(self, rating = 1500, rd = 350, vol = 0.06):
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self.setRating(rating)
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self.setRd(rd)
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self.vol = vol
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def _preRatingRD(self):
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self.__rd = math.sqrt(math.pow(self.__rd, 2) + math.pow(self.vol, 2))
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def update_player(self, rating_list, RD_list, outcome_list):
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rating_list = [(x - 1500) / 173.7178 for x in rating_list]
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RD_list = [x / 173.7178 for x in RD_list]
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v = self._v(rating_list, RD_list)
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self.vol = self._newVol(rating_list, RD_list, outcome_list, v)
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self._preRatingRD()
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self.__rd = 1 / math.sqrt((1 / math.pow(self.__rd, 2)) + (1 / v))
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tempSum = 0
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for i in range(len(rating_list)):
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tempSum += self._g(RD_list[i]) * \
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(outcome_list[i] - self._E(rating_list[i], RD_list[i]))
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self.__rating += math.pow(self.__rd, 2) * tempSum
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def _newVol(self, rating_list, RD_list, outcome_list, v):
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i = 0
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delta = self._delta(rating_list, RD_list, outcome_list, v)
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a = math.log(math.pow(self.vol, 2))
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tau = self._tau
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x0 = a
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x1 = 0
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while x0 != x1:
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# New iteration, so x(i) becomes x(i-1)
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x0 = x1
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d = math.pow(self.__rating, 2) + v + math.exp(x0)
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h1 = -(x0 - a) / math.pow(tau, 2) - 0.5 * math.exp(x0) \
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/ d + 0.5 * math.exp(x0) * math.pow(delta / d, 2)
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h2 = -1 / math.pow(tau, 2) - 0.5 * math.exp(x0) * \
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(math.pow(self.__rating, 2) + v) \
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/ math.pow(d, 2) + 0.5 * math.pow(delta, 2) * math.exp(x0) \
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* (math.pow(self.__rating, 2) + v - math.exp(x0)) / math.pow(d, 3)
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x1 = x0 - (h1 / h2)
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return math.exp(x1 / 2)
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def _delta(self, rating_list, RD_list, outcome_list, v):
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tempSum = 0
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for i in range(len(rating_list)):
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tempSum += self._g(RD_list[i]) * (outcome_list[i] - self._E(rating_list[i], RD_list[i]))
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return v * tempSum
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def _v(self, rating_list, RD_list):
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tempSum = 0
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for i in range(len(rating_list)):
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tempE = self._E(rating_list[i], RD_list[i])
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tempSum += math.pow(self._g(RD_list[i]), 2) * tempE * (1 - tempE)
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return 1 / tempSum
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def _E(self, p2rating, p2RD):
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return 1 / (1 + math.exp(-1 * self._g(p2RD) * \
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(self.__rating - p2rating)))
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def _g(self, RD):
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return 1 / math.sqrt(1 + 3 * math.pow(RD, 2) / math.pow(math.pi, 2))
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def did_not_compete(self):
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self._preRatingRD()
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@ -7,10 +7,12 @@
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# current benchmark of optimization: 1.33 times faster
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# setup:
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__version__ = "1.1.13.007"
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__version__ = "1.1.13.008"
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# changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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1.1.13.008:
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- moved Glicko2 to a seperate package
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1.1.13.007:
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- fixed bug with trueskill
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1.1.13.006:
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@ -271,7 +273,6 @@ __all__ = [
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'SVM',
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'random_forest_classifier',
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'random_forest_regressor',
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'Glicko2',
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# all statistics functions left out due to integration in other functions
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]
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@ -280,6 +281,7 @@ __all__ = [
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# imports (now in alphabetical order! v 1.0.3.006):
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import csv
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from analysis import glicko2 as Glicko2
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import numba
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from numba import jit
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import numpy as np
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@ -452,7 +454,7 @@ def elo(starting_score, opposing_score, observed, N, K):
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def glicko2(starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations):
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player = Glicko2(rating = starting_score, rd = starting_rd, vol = starting_vol)
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player = Glicko2.Glicko2(rating = starting_score, rd = starting_rd, vol = starting_vol)
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player.update_player([x for x in opposing_score], [x for x in opposing_rd], observations)
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@ -690,103 +692,4 @@ def random_forest_regressor(data, outputs, test_size, n_estimators="warn", crite
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kernel.fit(data_train, outputs_train)
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predictions = kernel.predict(data_test)
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return kernel, RegressionMetrics(predictions, outputs_test)
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class Glicko2:
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_tau = 0.5
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def getRating(self):
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return (self.__rating * 173.7178) + 1500
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def setRating(self, rating):
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self.__rating = (rating - 1500) / 173.7178
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rating = property(getRating, setRating)
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def getRd(self):
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return self.__rd * 173.7178
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def setRd(self, rd):
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self.__rd = rd / 173.7178
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rd = property(getRd, setRd)
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def __init__(self, rating = 1500, rd = 350, vol = 0.06):
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self.setRating(rating)
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self.setRd(rd)
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self.vol = vol
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def _preRatingRD(self):
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self.__rd = math.sqrt(math.pow(self.__rd, 2) + math.pow(self.vol, 2))
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def update_player(self, rating_list, RD_list, outcome_list):
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rating_list = [(x - 1500) / 173.7178 for x in rating_list]
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RD_list = [x / 173.7178 for x in RD_list]
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v = self._v(rating_list, RD_list)
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self.vol = self._newVol(rating_list, RD_list, outcome_list, v)
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self._preRatingRD()
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self.__rd = 1 / math.sqrt((1 / math.pow(self.__rd, 2)) + (1 / v))
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tempSum = 0
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for i in range(len(rating_list)):
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tempSum += self._g(RD_list[i]) * \
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(outcome_list[i] - self._E(rating_list[i], RD_list[i]))
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self.__rating += math.pow(self.__rd, 2) * tempSum
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def _newVol(self, rating_list, RD_list, outcome_list, v):
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i = 0
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delta = self._delta(rating_list, RD_list, outcome_list, v)
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a = math.log(math.pow(self.vol, 2))
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tau = self._tau
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x0 = a
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x1 = 0
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while x0 != x1:
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# New iteration, so x(i) becomes x(i-1)
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x0 = x1
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d = math.pow(self.__rating, 2) + v + math.exp(x0)
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h1 = -(x0 - a) / math.pow(tau, 2) - 0.5 * math.exp(x0) \
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/ d + 0.5 * math.exp(x0) * math.pow(delta / d, 2)
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h2 = -1 / math.pow(tau, 2) - 0.5 * math.exp(x0) * \
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(math.pow(self.__rating, 2) + v) \
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/ math.pow(d, 2) + 0.5 * math.pow(delta, 2) * math.exp(x0) \
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* (math.pow(self.__rating, 2) + v - math.exp(x0)) / math.pow(d, 3)
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x1 = x0 - (h1 / h2)
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return math.exp(x1 / 2)
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def _delta(self, rating_list, RD_list, outcome_list, v):
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tempSum = 0
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for i in range(len(rating_list)):
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tempSum += self._g(RD_list[i]) * (outcome_list[i] - self._E(rating_list[i], RD_list[i]))
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return v * tempSum
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def _v(self, rating_list, RD_list):
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tempSum = 0
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for i in range(len(rating_list)):
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tempE = self._E(rating_list[i], RD_list[i])
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tempSum += math.pow(self._g(RD_list[i]), 2) * tempE * (1 - tempE)
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return 1 / tempSum
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def _E(self, p2rating, p2RD):
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return 1 / (1 + math.exp(-1 * self._g(p2RD) * \
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(self.__rating - p2rating)))
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def _g(self, RD):
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return 1 / math.sqrt(1 + 3 * math.pow(RD, 2) / math.pow(math.pi, 2))
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def did_not_compete(self):
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self._preRatingRD()
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return kernel, RegressionMetrics(predictions, outputs_test)
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99
analysis-master/analysis-amd64/build/lib/analysis/glicko2.py
Normal file
99
analysis-master/analysis-amd64/build/lib/analysis/glicko2.py
Normal file
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import math
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class Glicko2:
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_tau = 0.5
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def getRating(self):
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return (self.__rating * 173.7178) + 1500
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def setRating(self, rating):
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self.__rating = (rating - 1500) / 173.7178
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rating = property(getRating, setRating)
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def getRd(self):
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return self.__rd * 173.7178
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def setRd(self, rd):
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self.__rd = rd / 173.7178
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rd = property(getRd, setRd)
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def __init__(self, rating = 1500, rd = 350, vol = 0.06):
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self.setRating(rating)
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self.setRd(rd)
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self.vol = vol
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def _preRatingRD(self):
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self.__rd = math.sqrt(math.pow(self.__rd, 2) + math.pow(self.vol, 2))
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def update_player(self, rating_list, RD_list, outcome_list):
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rating_list = [(x - 1500) / 173.7178 for x in rating_list]
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RD_list = [x / 173.7178 for x in RD_list]
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v = self._v(rating_list, RD_list)
|
||||
self.vol = self._newVol(rating_list, RD_list, outcome_list, v)
|
||||
self._preRatingRD()
|
||||
|
||||
self.__rd = 1 / math.sqrt((1 / math.pow(self.__rd, 2)) + (1 / v))
|
||||
|
||||
tempSum = 0
|
||||
for i in range(len(rating_list)):
|
||||
tempSum += self._g(RD_list[i]) * \
|
||||
(outcome_list[i] - self._E(rating_list[i], RD_list[i]))
|
||||
self.__rating += math.pow(self.__rd, 2) * tempSum
|
||||
|
||||
|
||||
def _newVol(self, rating_list, RD_list, outcome_list, v):
|
||||
|
||||
i = 0
|
||||
delta = self._delta(rating_list, RD_list, outcome_list, v)
|
||||
a = math.log(math.pow(self.vol, 2))
|
||||
tau = self._tau
|
||||
x0 = a
|
||||
x1 = 0
|
||||
|
||||
while x0 != x1:
|
||||
# New iteration, so x(i) becomes x(i-1)
|
||||
x0 = x1
|
||||
d = math.pow(self.__rating, 2) + v + math.exp(x0)
|
||||
h1 = -(x0 - a) / math.pow(tau, 2) - 0.5 * math.exp(x0) \
|
||||
/ d + 0.5 * math.exp(x0) * math.pow(delta / d, 2)
|
||||
h2 = -1 / math.pow(tau, 2) - 0.5 * math.exp(x0) * \
|
||||
(math.pow(self.__rating, 2) + v) \
|
||||
/ math.pow(d, 2) + 0.5 * math.pow(delta, 2) * math.exp(x0) \
|
||||
* (math.pow(self.__rating, 2) + v - math.exp(x0)) / math.pow(d, 3)
|
||||
x1 = x0 - (h1 / h2)
|
||||
|
||||
return math.exp(x1 / 2)
|
||||
|
||||
def _delta(self, rating_list, RD_list, outcome_list, v):
|
||||
|
||||
tempSum = 0
|
||||
for i in range(len(rating_list)):
|
||||
tempSum += self._g(RD_list[i]) * (outcome_list[i] - self._E(rating_list[i], RD_list[i]))
|
||||
return v * tempSum
|
||||
|
||||
def _v(self, rating_list, RD_list):
|
||||
|
||||
tempSum = 0
|
||||
for i in range(len(rating_list)):
|
||||
tempE = self._E(rating_list[i], RD_list[i])
|
||||
tempSum += math.pow(self._g(RD_list[i]), 2) * tempE * (1 - tempE)
|
||||
return 1 / tempSum
|
||||
|
||||
def _E(self, p2rating, p2RD):
|
||||
|
||||
return 1 / (1 + math.exp(-1 * self._g(p2RD) * \
|
||||
(self.__rating - p2rating)))
|
||||
|
||||
def _g(self, RD):
|
||||
|
||||
return 1 / math.sqrt(1 + 3 * math.pow(RD, 2) / math.pow(math.pi, 2))
|
||||
|
||||
def did_not_compete(self):
|
||||
|
||||
self._preRatingRD()
|
||||
BIN
analysis-master/analysis-amd64/dist/analysis-1.0.0.10-py3-none-any.whl
vendored
Normal file
BIN
analysis-master/analysis-amd64/dist/analysis-1.0.0.10-py3-none-any.whl
vendored
Normal file
Binary file not shown.
BIN
analysis-master/analysis-amd64/dist/analysis-1.0.0.10.tar.gz
vendored
Normal file
BIN
analysis-master/analysis-amd64/dist/analysis-1.0.0.10.tar.gz
vendored
Normal file
Binary file not shown.
@ -8,7 +8,7 @@ with open("requirements.txt", 'r') as file:
|
||||
|
||||
setuptools.setup(
|
||||
name="analysis",
|
||||
version="1.0.0.009",
|
||||
version="1.0.0.010",
|
||||
author="The Titan Scouting Team",
|
||||
author_email="titanscout2022@gmail.com",
|
||||
description="analysis package developed by Titan Scouting for The Red Alliance",
|
||||
|
||||
4
data analysis/requirements.txt
Normal file
4
data analysis/requirements.txt
Normal file
@ -0,0 +1,4 @@
|
||||
requests
|
||||
pymongo
|
||||
pandas
|
||||
dnspython
|
||||
@ -3,12 +3,15 @@
|
||||
# Notes:
|
||||
# setup:
|
||||
|
||||
__version__ = "0.0.5.000"
|
||||
__version__ = "0.0.5.001"
|
||||
|
||||
# changelog should be viewed using print(analysis.__changelog__)
|
||||
__changelog__ = """changelog:
|
||||
0.0.5.001:
|
||||
- text fixes
|
||||
- removed matplotlib requirement
|
||||
0.0.5.000:
|
||||
improved user interface
|
||||
- improved user interface
|
||||
0.0.4.002:
|
||||
- removed unessasary code
|
||||
0.0.4.001:
|
||||
@ -84,7 +87,6 @@ __all__ = [
|
||||
from analysis import analysis as an
|
||||
import data as d
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from os import system, name
|
||||
from pathlib import Path
|
||||
import time
|
||||
|
||||
Loading…
Reference in New Issue
Block a user